混合超启发式法求解大规模VRP的优化研究

车辆路径是一类NP(non-deteministic polynomial)完全问题,研究解决车辆路径问题的高质量启发式算法有着重要理论价值和现实意义.提出一种将最近邻搜索法和禁忌搜索法优势相结合的混合超启发式算法,用来解决带容量约束的车辆路径问题.先利用最近邻搜索法构建初步路线,再利用禁忌搜索法对内部线路和互跨线路进...     

变道约束下近邻交织区交通均衡组织方法

针对相邻较近的交织区车辆持续交织运行特点,以提高交织区通行效率及交通安全为目标,提出一种基于变道约束下近邻交织区交通均衡组织方法,旨在计算不同时段交通量所对应近邻各交织区的最佳提前变道诱导距离,以减少近邻交织区车辆相互干扰,降低平均交通延误,提高道路通行效率.对重庆市海峡路两段近邻交织区的日均交通量(4 092 pcu/h),早高峰时段交通量(5 340 pcu/h),晚高峰时段交通量(4 596 pcu/h),以及年均交通量(3 276 pcu/h)进行仿真建模分析.实验选取近邻交织区内累计平均延误作为近邻交织区的评价指标,利用仿真软件(Vissim 4.3)持续仿真 40次.结果表明,最佳提前变道约束距离为交织区长度的 60%,其相应的平均交通延误分别降低了57%、73%、63%和72%.     

An Adaptive Scheme for Neighbor Discovery in Mobile Ad Hoc Networks

The neighbor knowledge in mobile ad hoc networks is important information. However, the accuracy of neighbor knowledge is paid in terms of energy consumption. In traditional schemes for neighbor discovery, a mobile node uses fixed period to send HELLO messages to notify its existence. An adaptive scheme was proposed. The objective is that when mobile nodes are distributed sparsely or move slowly, fewer HELLO messages are needed to achieve reasonable accuracy, while in a mutable network where nodes are dense or move quickly, they can adaptively send more HELLO messages to ensure the accuracy. Simulation results show that the adaptive scheme achieves the objective and performs effectively.     

基于最近邻方法的LFM信号识别初探

在对LFM信号实现分选的基础上,选取待识信号的瞬时频率作为识别特征,利用最近邻方法对信号进行识别。实验结果表明,在一定的信噪比条件下能达到较理想的识别效果。该方法原理简单,运算量小,能够满足工程中实时性的要求。     

周期分类和Single-Pass聚类相结合的话题识别与跟踪方法

针对增量式聚类初始时话题模型不够充分和准确,随处理报道数量增加,误检与漏检的累积效应被放大的问题,提出了周期分类和Single—Pass聚类相结合的话题识别与跟踪方法．首先采用增量式聚类算法进行话题识别与跟踪,当新闻文本每积累到一定程度之后,对已经聚类的报道进行周期分类,使话题簇精度提高,从而提高后续话题识别与跟踪精度．实验表明这种方法是有效的,能够降低漏检率与错检率,减少归一化错误识别代价．     

一种模糊特征提取算法及其在人脸识别中的应用

F isherface是人脸特征提取中常用的方法,KPCA LDA能更好地解决非线性问题。本文把模糊技术与KPCA LDA相结合提出了一种新的特征抽取方法。首先用KPCA进行初次特征提取,然后利用FKNN计算图像对各类别的隶属程度,再在此基础上用LDA进行二次特征提取。在ORL人脸库上的实验结果表明了该方法的有效性。     

Short-term traffic flow rate forecasting based on identifying similar traffic patterns

The ability to timely and accurately forecast the evolution of traffic is very important in traffic management and control applications. This paper proposes a non-parametric and data-driven methodology for short-term traffic forecasting based on identifying similar traffic patterns using an enhanced K-nearest neighbor (K-NN) algorithm. Weighted Euclidean distance, which gives more weight to recent measurements, is used as a similarity measure for K-NN. Moreover, winsorization of the neighbors is implemented to dampen the effects of dominant candidates, and rank exponent is used to aggregate the candidate values. Robustness of the proposed method is demonstrated by implementing it on large datasets collected from different regions and by comparing it with advanced time series models, such as SARIMA and adaptive Kalman Filter models proposed by others. It is demonstrated that the proposed method reduces the mean absolute percent error by more than 25%. In addition, the effectiveness of the proposed enhanced K-NN algorithm is evaluated for multiple forecast steps and also its performance is tested under data with missing values. This research provides strong evidence suggesting that the proposed non-parametric and data-driven approach for short-term traffic forecasting provides promising results. Given the simplicity, accuracy, and robustness of the proposed approach, it can be easily incorporated with real-time traffic control for proactive freeway traffic management.     

Designing an integrated distribution system for catering services for high-speed railways: A three-echelon location routing model with tight time windows and time deadlines

An emerging task in catering services for high-speed railways (CSHR) is to design a distribution system for the delivery of high-quality perishable food products to trains in need. This paper proposes a novel model for integrating location decision making with daily rail catering operations, which are affected by various aspects of rail planning, to meet time-sensitive passenger demands. A three-echelon location routing problem with time windows and time budget constraints (3E-LRPTWTBC) is thus proposed toward formulating this integrated distribution system design problem. This model attempts to determine the capacities/locations of distribution centers and to optimize the number of meals delivered to stations. The model also attempts to generate a schedule for refrigerated cars traveling from distribution centers to rail stations for train loading whereby meals can be catered to trains within tight time windows and sold before a specified time deadline. By relaxing the time-window constraints, a relaxation model that can be solved using an off-the-shelf mixed integer programming (MIP) solver is obtained to provide a lower bound on the 3E-LRPTWTBC. A hybrid cross entropy algorithm (HCEA) is proposed to solve the 3E-LRPTWTBC. A small-scale case study is implemented, which reveals a 9.3% gap between the solution obtained using the HCEA and that obtained using the relaxation model (RM). A comparative analysis of the HCEA and an exhaustive enumeration algorithm indicates that the HCEA shows good performance in terms of computation time. Finally, a case study considering 156 trains on the Beijing-Shanghai high-speed corridor and a large-scale case study considering 1130 trains on the Chinese railway network are addressed in a comprehensive study to demonstrate the applicability of the proposed models and algorithm.     

基于数据融合的路段行程时间估计

基于检测器数据的路段行程时间估计通常具有精度不高和可靠性差的特点。论文引入了自适应式卡尔曼滤波,采用K近邻法寻找相似的交通流状态来标定状态转移系数,建立了基于固定型检测器数据和移动型检测器数据的路段行程时间估计融合模型。实际数据的验证结果是,平均相对误差为9.52%,相对误差的标准差为8.92%。研究表明,与基于移动检测器数据的估计方法相比较,该方法极大地改善了估计精度和可靠性,还具有收敛速度快、对初值不敏感、参数少等特点。     

基于双层K近邻算法的短时交通流预测

双层K近邻算法在K近邻算法的基础上,增加了模式匹配步骤,从而提高了K近邻算法的预测精度.鉴于此,利用双层K近邻算法,对北京市微波检测器数据进行分析,进而标定算法的最优参数.同时定义了预测算法的滞后性,并将双层K近邻算法与自适应预测算法的滞后性进行了对比,从预测精度及滞后性两方面验证了双层K近邻算法的适用性.